Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy

Baruah, Sasanka Kumar; Das, Nabajeet; Baruah, Sanjib; Rajeev, T P; Bagchi, Puskal Kumar; Sharma, Debanga; Phukan, Mandeep

doi:10.14740/wjon1230

Cited by 14 publications

(21 citation statements)

References 24 publications

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“…Jordan et al also support the use of PSA density, noting that PI-RADS v2 + PSAD is better than PI-RADSv2 alone [19]. Similar findings have been proposed by Schoots et al in active surveillance population, by Baruah et al, and by Maggi et al who conducted a meta-analysis, although a threshold of 0.15 ng/ml/ml was used [24,[31][32]. There is also evidence that points to combining the 4K-score test or the European Randomized Study of Screening for Prostate Cancer risk calculator (ERSPC-RC) with mpMRI to better identify clinically significant disease [33].…”

Section: Discussionsupporting

confidence: 55%

Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices

Ackerman¹

2021

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Purpose: The purpose of this study was to validate the second version of the Prostate Imaging Reporting and Data System (PI-RADSv2) scores in predicting positive in-bore MRI-guided targeted prostate biopsy results across different non-university related institutions. The study focuses on PI-RADS v2 scoring because during the study period, PI-RADS v2.1 had not been released. Materials and Methods: This was a retrospective review of 147 patients who underwent multiparametric magnetic resonance imaging (mpMRI) of the pelvis followed by in-bore MRI-guided targeted prostate biopsy from December 2014 to May 2018. All lesions on mpMRI were rated according to PI-RADS v2 criteria. PI-RADS v2 scores were then compared to MR-guided biopsy results and pre-biopsy PSA values. Results: Prostate Cancer (PCa) was detected in 54% (80/147) of patients, with more prostate cancer being detected with each subsequent increase in PI-RADS scores. Specifically, biopsy results in patients with PI-RADS 3, 4, and 5 lesions resulted in PCa in 25.6% (10/39), 58.1% (33/55), and 86.0% (37/43) respectively. Clinically significant PCa (Gleason score ≥7) was detected in 17.9% (7/39), 52.7% (29/55), and 72% (31/43) of cases for PI-RADS 3, 4, and 5 lesions respectively. When the PI-RADS scoring and biopsy results were compared across different institutions, there was no difference in the PI-RADS scoring of lesions or in the positive biopsy rates of the lesions. The sensitivity, specificity, PPV, and NPV for PI-RADS 3-4 lesions were also not statistically different across the institutions for detecting Gleason 7 or greater lesions. Conclusion: Our results agree with prior studies that higher PI-RADS scores are associated with the presence of clinically significant PCa and suggest prostate lesions with PI-RADS scores 3-5 have sufficient evidence to warrant targeted biopsy. The comparison of PI-RADS score across different types of non-university practices revealed no difference in scoring and biopsy outcome, suggesting that PI-RADS v2 can be easily applied outside of the university medical center setting. Clinical Relevance: PI-RADS v2 can be applied homogeneously in the non-university setting without significant difference in outcome.

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Section: Discussionsupporting

confidence: 55%

Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices

Ackerman¹

2021

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“…1 The Prostate Imaging-Reporting and Data System (PI-RADS) has standardised the diagnosis of prostate cancer using MRI and is effective for characterisation of prostate cancer. 2 However, MRI is still restricted by benign confounding appearances and substantial intra-reader and inter-reader variability. 3 Therefore, Gleason grade grouping (GGG), which assigns a prostate cancer lesion to one of five categories (1−5) based on invasive biopsies, remains the standard of care in determining the aggressiveness of prostate cancer.…”

Section: Introductionmentioning

confidence: 99%

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

Hiremath

Shiradkar

et al. 2021

The Lancet Digital Health

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Background Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging-Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI.Methods In this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong's test. We used log-rank tests and Kaplan-Meier curves to analyse survival. FindingsWe investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0•81 (95% CI 0•76−0•85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0•74 [95% CI 0•69−0•80], p=0•0005) and PIN (0•76...

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“…In 2016, the Prostate Imaging-Reporting and Data System (PI-RADS) was released to improve the reporting standardization of mpMRI. 5 Patients who had positive results on the mpMRI were suggested to undergo MRI targeted biopsy through real-time ultrasonographic guidance. 6 Multiple previous studies have proved that prostate biopsy procedures with needles targeted at MRI-identified lesions could improve the cancer detection rate and quantity of cancer per biopsy core when compared with systematic 12-core biopsy.…”

Section: Introductionmentioning

confidence: 99%

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA‐related parameters to enhance the detection of clinically significant cancer across different region

et al. 2022

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Objective: Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted. Patients and Methods: A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA],prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa.Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ 2 test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.Results: A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

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Combining Prostate-Specific Antigen Parameters With Prostate Imaging Reporting and Data System Score Version 2.0 to Improve Its Diagnostic Accuracy

Cited by 14 publications

References 24 publications

Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices

Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA‐related parameters to enhance the detection of clinically significant cancer across different region

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